Power electronic systems are used in many applications. Such a power electronic system can include a converter circuit having a multiplicity of driveable power semiconductor switches and an associated drive circuit for the driveable power semiconductor circuit. One or more loads which can, however, vary greatly over time, for example as a result of faults, can be connected to the converter circuit. Such a load may be, for example, one or more motors, or any electrical load. States of the power electronic system, for example an inductive load current and a capacitive load voltage, can be specifically affected by such variations and may be detected with difficulty, that is to say with a considerable amount of effort, or not at all, for example by measurement.
It is thus known to estimate states of the power electronic system, the estimated states then being able to be processed further in a control unit. A known method for estimating states in a power electronic system is the use of a time-discrete Kalman filter, as is stated, for example, in “Braided extended Kalman filters for sensorless estimation in induction motors at high-low/zero speed”, IET Control Theory, Appl., 2007. To estimate states, for example using a time-discrete Kalman filter, the following method steps are first effected:    (a) determination of output variable vectors y(k) for the sampling times k=−N+1 to k=0, where N is a predefinable sampling horizon and y is an output variable, for example the converter output voltage, which can be determined by measurement, for example,    (b) determination of manipulated variable vectors u(k) for the sampling times k=−N+1 to k=0, where the manipulated variable is, for example, the control factor of the converter circuit,    (c) determination of a first system model function f(x(k), u(k)) at the sampling time k for describing the power electronic system, which function is dependent on the manipulated variable vector u(k) and a system state vector x(k) at the sampling time k, and    (d) determination of a second system model function g(x(k), u(k)) at the sampling time k for describing the power electronic system, which function is dependent on the manipulated variable vector u(k) and the system state vector x(k) at the sampling time k.
In using a time-discrete Kalman filter to estimate states x of the power electronic system, the use of secondary conditions of the states (for example the fact that the inductive load current and/or the capacitive load voltage is/are limited or may not be negative), can either involve a very large amount of effort, or such conditions cannot be taken into account at all. System model functions f(x(k), u(k)), g(x(k), u(k)) for the Kalman filter which are piecewise affine-linear and describe a given power electronic system either cannot be taken into account, or can involve a very large amount of expenditure during estimation by the time-discrete Kalman filter.